The sad part is that open-sourced TensorFlow isn't same as TensorFlow that Google uses.
I've also heard Google is not really fond of cooperation with other corporations (say, NVidia) on TensorFlow
I've also heard Google is not really fond of cooperation with other corporations (say, NVidia) on TensorFlow
We daily sync the code between the two repositories using a suite of tools we've built. I'm on sync rotation this week and you can see all of my commits and activity on GitHub as proof; I merged something like 60 commits from the community just this week. It wouldn't make any sense for anybody (Google or the community) to maintain two different versions for something that is actively being developed by so many contributors. I've also directly worked with NVidia engineers on improvements they've made (and merged) to the system; the ones I've dealt with are great, so that statement is also false.
I'll be giving a talk about all the work we do to make this possible at OSCON next week, and if you are there feel free to catch me to ask me questions.
About internal version, this is pure speculation, based on idea that TPU are programmed with some framework, thus out should be TF, thus there is closed part and there should (could) be others.
Regarding internal version: we built TensorFlow to support devices as modular plugins: the CPU and GPU devices are built this way (you can read the source code to see how device registration works), and the same registration mechanism is used for the TPU code, which can't be opensourced due to internal dependencies. Internal customers just link in an additional library to get TPU support, but it still uses the same core codebase that is available in the opensource world. I know this because I wrote a lot of the device modularity and TPU binding code :)
I'd really like to talk more about birth Tensorflow and TPU, but unfortunately I won't come to OSCON. May be some other time :)